It is a powerful learning algorithm inspired by how the brain works.（这是一个强大的学习算法，灵感来自大脑的工作方式。
）
Example 1 – single neural network
Given data about the size of houses on the real estate（房地产） market and you want to fit a function that will predict their price. It is a
linear regression problem
because the price as a function of size
is a continuous output
.
We know the prices can never be negative（负数） so we are creating a function called Rectified Linear Unit (ReLU) which starts at zero.
The input is the size of the house (x)
The output is the price (y)
The “neuron”（神经元） implements the function ReLU (blue line)
Example 2 – Multiple neural network
The price of a house can be affected by other features such as size, number of bedrooms, zip code and wealth. The role of the neural network is to predicted the price and it will automatically generate the hidden units. We only need to give the inputs x and the output y.